1. IOU(Intersection Over Union)交并比
  2. IOU(Intersection Over Union)

IOU(Intersection Over Union)交并比

IOU(Intersection Over Union)

IOU用来衡量预测的物体框和真实框的重合程度。

IoU (intersection over union)Union就是并集, intersection就是交集,简单来讲就是模型产生的目标窗口和原来标记窗口的交叠率。

检测结果(DetectionResult)与 Ground Truth 的交集比上它们的并集,即为检测的准确率 IoU :

蓝色的框是:GroundTruth
黄色的框是:DetectionResult
绿色的框是:DetectionResult ⋂GroundTruth
红色的框是:DetectionResult ⋃GroundTruth

当然最理想的情况就是 DR 与 GT 完全重合,即IoU=1
评价一个算法的时候,一种常见的方法是先设置一个IOU的阈值,只要算法找到的IOU大于这个阈值,就是一个有效的检测.一般来说,这个score > 0.5 就可以被认为一个不错的结果了。

让我们继续并定义bb_intersection_over_union函数,它负责计算两个边界框之间的交集:

intersection_over_union.py

# import the necessary packages
from collections import namedtuple
import numpy as np
import cv2

# define the `Detection` object
Detection = namedtuple("Detection", ["image_path", "gt", "pred"])

def bb_intersection_over_union(boxA, boxB):
    # determine the (x, y)-coordinates of the intersection rectangle
    xA = max(boxA[0], boxB[0])
    yA = max(boxA[1], boxB[1])
    xB = min(boxA[2], boxB[2])
    yB = min(boxA[3], boxB[3])

    # compute the area of intersection rectangle
    interArea = (xB - xA + 1) * (yB - yA + 1)

    # compute the area of both the prediction and ground-truth
    # rectangles
    boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
    boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)

    # compute the intersection over union by taking the intersection
    # area and dividing it by the sum of prediction + ground-truth
    # areas - the interesection area
    iou = interArea / float(boxAArea + boxBArea - interArea)

    # return the intersection over union value
    return iou

# define the list of example detections
examples = [
    Detection("image_0002.jpg", [39, 63, 203, 112], [54, 66, 198, 114]),
    Detection("image_0016.jpg", [49, 75, 203, 125], [42, 78, 186, 126]),
    Detection("image_0075.jpg", [31, 69, 201, 125], [18, 63, 235, 135]),
    Detection("image_0090.jpg", [50, 72, 197, 121], [54, 72, 198, 120]),
    Detection("image_0120.jpg", [35, 51, 196, 110], [36, 60, 180, 108])]

# loop over the example detections
for detection in examples:
    # load the image
    image = cv2.imread(detection.image_path)

    # draw the ground-truth bounding box along with the predicted
    # bounding box
    cv2.rectangle(image, tuple(detection.gt[:2]), 
        tuple(detection.gt[2:]), (0, 255, 0), 2)
    cv2.rectangle(image, tuple(detection.pred[:2]), 
        tuple(detection.pred[2:]), (0, 0, 255), 2)

    # compute the intersection over union and display it
    iou = bb_intersection_over_union(detection.gt, detection.pred)
    cv2.putText(image, "IoU: {:.4f}".format(iou), (10, 30),
        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
    print("{}: {:.4f}".format(detection.image_path, iou))

    # show the output image
    cv2.imshow("Image", image)
    cv2.waitKey(0)

运行结果:python intersection_over_union.py

Intersection over Union (IoU) for object detection - PyImageSearch


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